http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
근전도 신호와 딥러닝을 이용한 Finger Motion Tracking
진성호(Sung-Ho Jin),심희동(Hee-Dong Sim),안장원(Jang-Won Ahn),최효은(Hyo-Eun Choi),박우진(Woo-Jin Park),지수빈(Soo-Been Ji),양석조(Seok-Jo Yang) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
VR controllers are generally used by hand. But they are not suitable for Handicapped with amputated arm joint. Controllors for them are needed because virtual reality technology can become close to our real life. This study aims to implement it through electromyography signal and Deep learning. Data sets are created through digital filtering and time series feature extraction. Using 1d CNN-LSTM, CNN is used to create a feature map of electromyogram signal and LSTM is used to analyze time series associations. This model takes about 370ms to calculate the predicted data. Loss of prediction through train data is 0.00164, and 0.008458 for test data and 0.008688 for validation data.